Sport provides a rich set of opportunities for the application of stochastic modelling. The discrete nature of some sports (tennis, for example) lends itself to analysis through Markov chains to model how the state of the game changes over time, enabling the estimation of the probabilities of various outcomes. In others there are clear "risk and reward" types of decision to be made by the player. There are opportunities to apply game theory, optimisation techniques, dynamic programming, Markov models, and statistical methods to describe and analyse games and sports, ranging from football, tennis and golf to darts and snooker. A good player is generally one who does not merely have good technical skills but is able to make excellent decisions under pressure. The development and application of stochastic models to sports is not merely a source of entertainment for the modeller, they may serve to inform spectators and viewers, and act as decision tools for those involved in in-play betting, in which there has been a huge upsurge of activity in recent years.